Overview

Dataset statistics

Number of variables16
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory132.7 B

Variable types

Categorical2
Numeric14

Alerts

Casos_entre_nascidos is highly overall correlated with Espinha bífidaprop and 10 other fieldsHigh correlation
Espinha bífidaprop is highly overall correlated with Casos_entre_nascidos and 7 other fieldsHigh correlation
Outras malformações congênitas do sistema nervosoprop is highly overall correlated with Casos_entre_nascidos and 5 other fieldsHigh correlation
Malformações congênitas do aparelho circulatórioprop is highly overall correlated with Ausência atresia e estenose do intestino delgadoprop and 4 other fieldsHigh correlation
Fenda labial e fenda palatinaprop is highly overall correlated with Casos_entre_nascidos and 3 other fieldsHigh correlation
Ausência atresia e estenose do intestino delgadoprop is highly overall correlated with Malformações congênitas do aparelho circulatórioprop and 2 other fieldsHigh correlation
Outras malformações congênitas do aparelho digestivoprop is highly overall correlated with UF and 1 other fieldsHigh correlation
Testiculo não-descidoprop is highly overall correlated with Casos_entre_nascidos and 5 other fieldsHigh correlation
Outras malformações do aparelho geniturinárioprop is highly overall correlated with Casos_entre_nascidos and 6 other fieldsHigh correlation
Deformidades congênitas do quadrilprop is highly overall correlated with UF and 1 other fieldsHigh correlation
Deformidades congênitas dos pésprop is highly overall correlated with Casos_entre_nascidos and 6 other fieldsHigh correlation
Outras malformações e deformidades congênitas do aparelho osteomuscularprop is highly overall correlated with Casos_entre_nascidos and 7 other fieldsHigh correlation
Outras malformações congênitasprop is highly overall correlated with Casos_entre_nascidos and 5 other fieldsHigh correlation
Anomalias cromossômicas não classificadas em outra parteprop is highly overall correlated with Casos_entre_nascidos and 2 other fieldsHigh correlation
UF is highly overall correlated with Casos_entre_nascidos and 14 other fieldsHigh correlation
Estado is highly overall correlated with Casos_entre_nascidos and 14 other fieldsHigh correlation
UF is uniformly distributedUniform
Estado is uniformly distributedUniform
UF has unique valuesUnique
Estado has unique valuesUnique
Casos_entre_nascidos has unique valuesUnique
Espinha bífidaprop has unique valuesUnique
Outras malformações congênitas do sistema nervosoprop has unique valuesUnique
Malformações congênitas do aparelho circulatórioprop has unique valuesUnique
Fenda labial e fenda palatinaprop has unique valuesUnique
Outras malformações congênitas do aparelho digestivoprop has unique valuesUnique
Testiculo não-descidoprop has unique valuesUnique
Outras malformações do aparelho geniturinárioprop has unique valuesUnique
Deformidades congênitas dos pésprop has unique valuesUnique
Outras malformações e deformidades congênitas do aparelho osteomuscularprop has unique valuesUnique
Outras malformações congênitasprop has unique valuesUnique
Anomalias cromossômicas não classificadas em outra parteprop has unique valuesUnique
Ausência atresia e estenose do intestino delgadoprop has 14 (51.9%) zerosZeros
Testiculo não-descidoprop has 1 (3.7%) zerosZeros
Deformidades congênitas do quadrilprop has 4 (14.8%) zerosZeros

Reproduction

Analysis started2023-04-16 17:09:07.045359
Analysis finished2023-04-16 17:09:26.247715
Duration19.2 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

UF
Categorical

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size344.0 B
AC
 
1
PB
 
1
SP
 
1
SE
 
1
SC
 
1
Other values (22)
22 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters54
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st rowAC
2nd rowAL
3rd rowAM
4th rowAP
5th rowBA

Common Values

ValueCountFrequency (%)
AC 1
 
3.7%
PB 1
 
3.7%
SP 1
 
3.7%
SE 1
 
3.7%
SC 1
 
3.7%
RS 1
 
3.7%
RR 1
 
3.7%
RO 1
 
3.7%
RN 1
 
3.7%
RJ 1
 
3.7%
Other values (17) 17
63.0%

Length

2023-04-16T14:09:26.325855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ac 1
 
3.7%
al 1
 
3.7%
am 1
 
3.7%
ap 1
 
3.7%
ba 1
 
3.7%
ce 1
 
3.7%
df 1
 
3.7%
es 1
 
3.7%
go 1
 
3.7%
ma 1
 
3.7%
Other values (17) 17
63.0%

Most occurring characters

ValueCountFrequency (%)
A 7
13.0%
P 7
13.0%
R 7
13.0%
S 6
11.1%
M 5
9.3%
E 4
7.4%
O 3
 
5.6%
C 3
 
5.6%
B 2
 
3.7%
G 2
 
3.7%
Other values (7) 8
14.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 54
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 7
13.0%
P 7
13.0%
R 7
13.0%
S 6
11.1%
M 5
9.3%
E 4
7.4%
O 3
 
5.6%
C 3
 
5.6%
B 2
 
3.7%
G 2
 
3.7%
Other values (7) 8
14.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 54
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 7
13.0%
P 7
13.0%
R 7
13.0%
S 6
11.1%
M 5
9.3%
E 4
7.4%
O 3
 
5.6%
C 3
 
5.6%
B 2
 
3.7%
G 2
 
3.7%
Other values (7) 8
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 7
13.0%
P 7
13.0%
R 7
13.0%
S 6
11.1%
M 5
9.3%
E 4
7.4%
O 3
 
5.6%
C 3
 
5.6%
B 2
 
3.7%
G 2
 
3.7%
Other values (7) 8
14.8%

Estado
Categorical

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size344.0 B
Acre
 
1
Paraíba
 
1
São Paulo
 
1
Sergipe
 
1
Santa Catarina
 
1
Other values (22)
22 

Length

Max length19
Median length16
Mean length9.4074074
Min length4

Characters and Unicode

Total characters254
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st rowAcre
2nd rowAlagoas
3rd rowAmazonas
4th rowAmapá
5th rowBahia

Common Values

ValueCountFrequency (%)
Acre 1
 
3.7%
Paraíba 1
 
3.7%
São Paulo 1
 
3.7%
Sergipe 1
 
3.7%
Santa Catarina 1
 
3.7%
Rio Grande do Sul 1
 
3.7%
Roraima 1
 
3.7%
Rondônia 1
 
3.7%
Rio Grande do Norte 1
 
3.7%
Rio de Janeiro 1
 
3.7%
Other values (17) 17
63.0%

Length

2023-04-16T14:09:26.412934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rio 3
 
6.8%
do 3
 
6.8%
sul 2
 
4.5%
grosso 2
 
4.5%
grande 2
 
4.5%
mato 2
 
4.5%
federal 1
 
2.3%
ceará 1
 
2.3%
bahia 1
 
2.3%
amapá 1
 
2.3%
Other values (26) 26
59.1%

Most occurring characters

ValueCountFrequency (%)
a 37
14.6%
o 27
 
10.6%
r 20
 
7.9%
i 17
 
6.7%
17
 
6.7%
n 15
 
5.9%
e 13
 
5.1%
s 12
 
4.7%
t 10
 
3.9%
d 8
 
3.1%
Other values (27) 78
30.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 197
77.6%
Uppercase Letter 40
 
15.7%
Space Separator 17
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 37
18.8%
o 27
13.7%
r 20
10.2%
i 17
8.6%
n 15
7.6%
e 13
 
6.6%
s 12
 
6.1%
t 10
 
5.1%
d 8
 
4.1%
u 5
 
2.5%
Other values (12) 33
16.8%
Uppercase Letter
ValueCountFrequency (%)
G 6
15.0%
S 6
15.0%
P 6
15.0%
R 5
12.5%
M 4
10.0%
A 4
10.0%
C 2
 
5.0%
J 1
 
2.5%
N 1
 
2.5%
E 1
 
2.5%
Other values (4) 4
10.0%
Space Separator
ValueCountFrequency (%)
17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 237
93.3%
Common 17
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 37
15.6%
o 27
 
11.4%
r 20
 
8.4%
i 17
 
7.2%
n 15
 
6.3%
e 13
 
5.5%
s 12
 
5.1%
t 10
 
4.2%
d 8
 
3.4%
G 6
 
2.5%
Other values (26) 72
30.4%
Common
ValueCountFrequency (%)
17
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 243
95.7%
None 11
 
4.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 37
15.2%
o 27
11.1%
r 20
 
8.2%
i 17
 
7.0%
17
 
7.0%
n 15
 
6.2%
e 13
 
5.3%
s 12
 
4.9%
t 10
 
4.1%
d 8
 
3.3%
Other values (23) 67
27.6%
None
ValueCountFrequency (%)
á 5
45.5%
í 3
27.3%
ã 2
 
18.2%
ô 1
 
9.1%

Casos_entre_nascidos
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0085486037
Minimum0.004311274
Maximum0.02200923
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:09:26.488497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.004311274
5-th percentile0.0050224316
Q10.00722975
median0.0076417991
Q30.0092047056
95-th percentile0.012171883
Maximum0.02200923
Range0.017697956
Interquartile range (IQR)0.0019749556

Descriptive statistics

Standard deviation0.0033039949
Coefficient of variation (CV)0.38649527
Kurtosis10.426475
Mean0.0085486037
Median Absolute Deviation (MAD)0.00122818
Skewness2.6724188
Sum0.2308123
Variance1.0916382 × 10-5
MonotonicityNot monotonic
2023-04-16T14:09:26.582555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.009150633505 1
 
3.7%
0.008255969902 1
 
3.7%
0.01263553653 1
 
3.7%
0.01109002491 1
 
3.7%
0.008335464076 1
 
3.7%
0.009258777633 1
 
3.7%
0.007198683669 1
 
3.7%
0.007573226417 1
 
3.7%
0.007641799072 1
 
3.7%
0.006760785521 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.004311273981 1
3.7%
0.004990298683 1
3.7%
0.005097408493 1
3.7%
0.005250815661 1
3.7%
0.006760785521 1
3.7%
0.0070766171 1
3.7%
0.007198683669 1
3.7%
0.007260816337 1
3.7%
0.007336580364 1
3.7%
0.007385282466 1
3.7%
ValueCountFrequency (%)
0.02200922968 1
3.7%
0.01263553653 1
3.7%
0.01109002491 1
3.7%
0.01040585949 1
3.7%
0.01036032288 1
3.7%
0.01002834421 1
3.7%
0.009258777633 1
3.7%
0.009150633505 1
3.7%
0.00888220073 1
3.7%
0.008869979087 1
3.7%

Espinha bífidaprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.024631267
Minimum0.010195759
Maximum0.04240472
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:09:26.673794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.010195759
5-th percentile0.011056004
Q10.020745118
median0.023665838
Q30.029168052
95-th percentile0.035360392
Maximum0.04240472
Range0.032208961
Interquartile range (IQR)0.0084229345

Descriptive statistics

Standard deviation0.0078954325
Coefficient of variation (CV)0.32054512
Kurtosis-0.069629063
Mean0.024631267
Median Absolute Deviation (MAD)0.00453836
Skewness0.013481017
Sum0.66504421
Variance6.2337855 × 10-5
MonotonicityNot monotonic
2023-04-16T14:09:26.758289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.01173158142 1
 
3.7%
0.02264295543 1
 
3.7%
0.03198653487 1
 
3.7%
0.03558296762 1
 
3.7%
0.02863717719 1
 
3.7%
0.02600780234 1
 
3.7%
0.01371177842 1
 
3.7%
0.02969892713 1
 
3.7%
0.02689196154 1
 
3.7%
0.02067665558 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.01019575856 1
3.7%
0.01076647014 1
3.7%
0.01173158142 1
3.7%
0.01371177842 1
3.7%
0.01783449588 1
3.7%
0.01945575761 1
3.7%
0.02067665558 1
3.7%
0.0208135797 1
3.7%
0.02151848797 1
3.7%
0.02264295543 1
3.7%
ValueCountFrequency (%)
0.04240471957 1
3.7%
0.03558296762 1
3.7%
0.0348410474 1
3.7%
0.03328617791 1
3.7%
0.03303297792 1
3.7%
0.03198653487 1
3.7%
0.02969892713 1
3.7%
0.02863717719 1
3.7%
0.0282041984 1
3.7%
0.02729513055 1
3.7%

Outras malformações congênitas do sistema nervosoprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.075825771
Minimum0.050479556
Maximum0.12157514
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:09:26.860213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.050479556
5-th percentile0.054989055
Q10.060136504
median0.072615699
Q30.082757446
95-th percentile0.10953097
Maximum0.12157514
Range0.071095584
Interquartile range (IQR)0.022620941

Descriptive statistics

Standard deviation0.018427678
Coefficient of variation (CV)0.24302659
Kurtosis0.20516977
Mean0.075825771
Median Absolute Deviation (MAD)0.01100669
Skewness0.84370994
Sum2.0472958
Variance0.00033957933
MonotonicityNot monotonic
2023-04-16T14:09:26.937083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.07625527921 1
 
3.7%
0.06967063209 1
 
3.7%
0.09459119669 1
 
3.7%
0.1215751394 1
 
3.7%
0.07261569931 1
 
3.7%
0.05498792495 1
 
3.7%
0.0754147813 1
 
3.7%
0.0816720496 1
 
3.7%
0.08291688143 1
 
3.7%
0.05866399954 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.05047955578 1
3.7%
0.05498792495 1
3.7%
0.05499169147 1
3.7%
0.05568728893 1
3.7%
0.0577759652 1
3.7%
0.05836727282 1
3.7%
0.05866399954 1
3.7%
0.06160900895 1
3.7%
0.06613688798 1
3.7%
0.06837540048 1
3.7%
ValueCountFrequency (%)
0.1215751394 1
3.7%
0.1101099264 1
3.7%
0.1081800782 1
3.7%
0.09466335345 1
3.7%
0.09459119669 1
3.7%
0.09445795073 1
3.7%
0.08291688143 1
3.7%
0.08259800959 1
3.7%
0.0816720496 1
3.7%
0.08094169885 1
3.7%

Malformações congênitas do aparelho circulatórioprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.062605656
Minimum0.0068558892
Maximum0.27180002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:09:27.019149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.0068558892
5-th percentile0.013133227
Q10.03593121
median0.047443957
Q30.082655797
95-th percentile0.11632728
Maximum0.27180002
Range0.26494413
Interquartile range (IQR)0.046724587

Descriptive statistics

Standard deviation0.051721952
Coefficient of variation (CV)0.82615461
Kurtosis9.9228905
Mean0.062605656
Median Absolute Deviation (MAD)0.017311078
Skewness2.702641
Sum1.6903527
Variance0.0026751603
MonotonicityNot monotonic
2023-04-16T14:09:27.108349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.05865790709 1
 
3.7%
0.04702767666 1
 
3.7%
0.2718000209 1
 
3.7%
0.04744395683 1
 
3.7%
0.09307082588 1
 
3.7%
0.1159204904 1
 
3.7%
0.006855889209 1
 
3.7%
0.0408360248 1
 
3.7%
0.04033794232 1
 
3.7%
0.05337462253 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.006855889209 1
3.7%
0.01183291918 1
3.7%
0.01616727743 1
3.7%
0.02548939641 1
3.7%
0.02775143961 1
3.7%
0.03013287865 1
3.7%
0.0342479552 1
3.7%
0.03761446471 1
3.7%
0.04033794232 1
3.7%
0.0408360248 1
3.7%
ValueCountFrequency (%)
0.2718000209 1
3.7%
0.1165016227 1
3.7%
0.1159204904 1
3.7%
0.1033315656 1
3.7%
0.09507436639 1
3.7%
0.09307082588 1
3.7%
0.09175827201 1
3.7%
0.07355332229 1
3.7%
0.06129660345 1
3.7%
0.05865790709 1
3.7%

Fenda labial e fenda palatinaprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06482036
Minimum0.040418194
Maximum0.091536989
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:09:27.188430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.040418194
5-th percentile0.040757698
Q10.054420927
median0.063168731
Q30.076853688
95-th percentile0.087920477
Maximum0.091536989
Range0.051118796
Interquartile range (IQR)0.022432761

Descriptive statistics

Standard deviation0.015038207
Coefficient of variation (CV)0.23199819
Kurtosis-0.90275415
Mean0.06482036
Median Absolute Deviation (MAD)0.010376614
Skewness0.12625873
Sum1.7501497
Variance0.00022614765
MonotonicityNot monotonic
2023-04-16T14:09:27.288296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.05279211638 1
 
3.7%
0.05399473987 1
 
3.7%
0.06961775236 1
 
3.7%
0.08599217175 1
 
3.7%
0.07568396829 1
 
3.7%
0.07802340702 1
 
3.7%
0.05484711367 1
 
3.7%
0.05939785425 1
 
3.7%
0.06947090066 1
 
3.7%
0.04135331115 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.04041819358 1
3.7%
0.04050243527 1
3.7%
0.04135331115 1
3.7%
0.0492415334 1
3.7%
0.04954946688 1
3.7%
0.05279211638 1
3.7%
0.05399473987 1
3.7%
0.05484711367 1
3.7%
0.0560766721 1
3.7%
0.05746670894 1
3.7%
ValueCountFrequency (%)
0.09153698927 1
3.7%
0.08874689386 1
3.7%
0.08599217175 1
3.7%
0.08595764632 1
3.7%
0.08325431882 1
3.7%
0.07856883839 1
3.7%
0.07802340702 1
3.7%
0.07568396829 1
3.7%
0.07290635996 1
3.7%
0.06961775236 1
3.7%

Ausência atresia e estenose do intestino delgadoprop
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0024526549
Minimum0
Maximum0.017597372
Zeros14
Zeros (%)51.9%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:09:27.369030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.0032902568
95-th percentile0.0091117603
Maximum0.017597372
Range0.017597372
Interquartile range (IQR)0.0032902568

Descriptive statistics

Standard deviation0.0040338119
Coefficient of variation (CV)1.6446716
Kurtosis7.0666887
Mean0.0024526549
Median Absolute Deviation (MAD)0
Skewness2.461322
Sum0.066221683
Variance1.6271638 × 10-5
MonotonicityNot monotonic
2023-04-16T14:09:27.438447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 14
51.9%
0.01759737213 1
 
3.7%
0.001025378108 1
 
3.7%
0.003096981991 1
 
3.7%
0.001802841278 1
 
3.7%
0.002729513055 1
 
3.7%
0.001469896519 1
 
3.7%
0.003483531604 1
 
3.7%
0.005860748613 1
 
3.7%
0.002885114731 1
 
3.7%
Other values (4) 4
 
14.8%
ValueCountFrequency (%)
0 14
51.9%
0.001025378108 1
 
3.7%
0.001469896519 1
 
3.7%
0.001802841278 1
 
3.7%
0.002729513055 1
 
3.7%
0.002885114731 1
 
3.7%
0.003096981991 1
 
3.7%
0.003483531604 1
 
3.7%
0.003712365891 1
 
3.7%
0.004458480401 1
 
3.7%
ValueCountFrequency (%)
0.01759737213 1
3.7%
0.009204806955 1
3.7%
0.008894651407 1
3.7%
0.005860748613 1
3.7%
0.004458480401 1
3.7%
0.003712365891 1
3.7%
0.003483531604 1
3.7%
0.003096981991 1
3.7%
0.002885114731 1
3.7%
0.002729513055 1
3.7%

Outras malformações congênitas do aparelho digestivoprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.097947988
Minimum0.022274195
Maximum1.4909478
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:09:27.531075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.022274195
5-th percentile0.027018431
Q10.033530027
median0.045285911
Q30.051834878
95-th percentile0.08965074
Maximum1.4909478
Range1.4686736
Interquartile range (IQR)0.01830485

Descriptive statistics

Standard deviation0.27884024
Coefficient of variation (CV)2.8468195
Kurtosis26.806634
Mean0.097947988
Median Absolute Deviation (MAD)0.011006465
Skewness5.1695215
Sum2.6445957
Variance0.07775188
MonotonicityNot monotonic
2023-04-16T14:09:27.612054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.05279211638 1
 
3.7%
0.04528591086 1
 
3.7%
0.0974990635 1
 
3.7%
0.04151346222 1
 
3.7%
0.05931986704 1
 
3.7%
0.04309864388 1
 
3.7%
0.03427944604 1
 
3.7%
0.02227419534 1
 
3.7%
0.06050691348 1
 
3.7%
0.03462137678 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.02227419534 1
3.7%
0.02694546238 1
3.7%
0.02718868951 1
3.7%
0.02912540568 1
3.7%
0.03124558284 1
3.7%
0.03158436535 1
3.7%
0.03281209946 1
3.7%
0.0342479552 1
3.7%
0.03427944604 1
3.7%
0.03462137678 1
3.7%
ValueCountFrequency (%)
1.490947817 1
3.7%
0.0974990635 1
3.7%
0.07133798351 1
3.7%
0.06050691348 1
3.7%
0.05931986704 1
3.7%
0.05408523834 1
3.7%
0.05279211638 1
3.7%
0.05087763928 1
3.7%
0.04928791927 1
3.7%
0.0490143337 1
3.7%

Testiculo não-descidoprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.017168575
Minimum0
Maximum0.037938029
Zeros1
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:09:27.688292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0055321019
Q10.0094360202
median0.016861657
Q30.021230508
95-th percentile0.034493835
Maximum0.037938029
Range0.037938029
Interquartile range (IQR)0.011794487

Descriptive statistics

Standard deviation0.010192728
Coefficient of variation (CV)0.59368516
Kurtosis-0.54630594
Mean0.017168575
Median Absolute Deviation (MAD)0.0069536033
Skewness0.52391414
Sum0.46355152
Variance0.00010389171
MonotonicityNot monotonic
2023-04-16T14:09:27.775667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.005865790709 1
 
3.7%
0.03483531604 1
 
3.7%
0.03369704475 1
 
3.7%
0.02965247302 1
 
3.7%
0.01943237024 1
 
3.7%
0.01709084154 1
 
3.7%
0 1
 
3.7%
0.02227419534 1
 
3.7%
0.008963987181 1
 
3.7%
0.01971495066 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0 1
3.7%
0.005389092477 1
3.7%
0.005865790709 1
3.7%
0.005879586077 1
3.7%
0.006511942903 1
3.7%
0.00849646547 1
3.7%
0.008963987181 1
3.7%
0.009908053266 1
3.7%
0.01025378108 1
3.7%
0.01075924398 1
3.7%
ValueCountFrequency (%)
0.03793802939 1
3.7%
0.03483531604 1
3.7%
0.03369704475 1
3.7%
0.03328617791 1
3.7%
0.02965247302 1
3.7%
0.0275258706 1
3.7%
0.02227419534 1
3.7%
0.02018681984 1
3.7%
0.01971495066 1
3.7%
0.01943237024 1
3.7%

Outras malformações do aparelho geniturinárioprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.077721718
Minimum0.033723313
Maximum0.13317023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:09:27.866698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.033723313
5-th percentile0.034773797
Q10.048670254
median0.074539667
Q30.10458461
95-th percentile0.12937806
Maximum0.13317023
Range0.099446912
Interquartile range (IQR)0.055914359

Descriptive statistics

Standard deviation0.032478118
Coefficient of variation (CV)0.41787699
Kurtosis-1.3013308
Mean0.077721718
Median Absolute Deviation (MAD)0.030025127
Skewness0.22931981
Sum2.0984864
Variance0.0010548281
MonotonicityNot monotonic
2023-04-16T14:09:27.938399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.05279211638 1
 
3.7%
0.1027641823 1
 
3.7%
0.1240119667 1
 
3.7%
0.1215751394 1
 
3.7%
0.09613909486 1
 
3.7%
0.1136912502 1
 
3.7%
0.03427944604 1
 
3.7%
0.04454839069 1
 
3.7%
0.08515787822 1
 
3.7%
0.07260872074 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.03372331319 1
3.7%
0.03427944604 1
3.7%
0.03592728318 1
3.7%
0.04078303426 1
3.7%
0.04141521713 1
3.7%
0.04409689558 1
3.7%
0.04454839069 1
3.7%
0.05279211638 1
3.7%
0.05639579595 1
3.7%
0.05746670894 1
3.7%
ValueCountFrequency (%)
0.1331702256 1
3.7%
0.1316778134 1
3.7%
0.1240119667 1
3.7%
0.1215751394 1
3.7%
0.1136912502 1
3.7%
0.1081800782 1
3.7%
0.1046044301 1
3.7%
0.1045647941 1
3.7%
0.1027641823 1
3.7%
0.09613909486 1
3.7%

Deformidades congênitas do quadrilprop
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0050695099
Minimum0
Maximum0.028394464
Zeros4
Zeros (%)14.8%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:09:28.032389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0017438597
median0.0040910253
Q30.0069524616
95-th percentile0.010295739
Maximum0.028394464
Range0.028394464
Interquartile range (IQR)0.0052086019

Descriptive statistics

Standard deviation0.005633964
Coefficient of variation (CV)1.1113429
Kurtosis11.117263
Mean0.0050695099
Median Absolute Deviation (MAD)0.0028468346
Skewness2.8487593
Sum0.13687677
Variance3.174155 × 10-5
MonotonicityNot monotonic
2023-04-16T14:09:28.106937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 4
 
14.8%
0.0020145856 1
 
3.7%
0.02839446411 1
 
3.7%
0.008895741905 1
 
3.7%
0.004091025313 1
 
3.7%
0.002972320268 1
 
3.7%
0.006855889209 1
 
3.7%
0.004481993591 1
 
3.7%
0.002404262276 1
 
3.7%
0.005209554323 1
 
3.7%
Other values (14) 14
51.9%
ValueCountFrequency (%)
0 4
14.8%
0.0008981820795 1
 
3.7%
0.001195471554 1
 
3.7%
0.001538067162 1
 
3.7%
0.001949652182 1
 
3.7%
0.0020145856 1
 
3.7%
0.002231827346 1
 
3.7%
0.002404262276 1
 
3.7%
0.002972320268 1
 
3.7%
0.003963221306 1
 
3.7%
ValueCountFrequency (%)
0.02839446411 1
3.7%
0.01081704767 1
3.7%
0.00907935355 1
3.7%
0.008895741905 1
3.7%
0.008321544479 1
3.7%
0.007742454978 1
3.7%
0.006967063209 1
3.7%
0.006937859901 1
3.7%
0.006855889209 1
3.7%
0.00550549632 1
3.7%

Deformidades congênitas dos pésprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12017404
Minimum0.066272431
Maximum0.20460206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:09:28.186034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.066272431
5-th percentile0.071307829
Q10.094588175
median0.11879571
Q30.14386973
95-th percentile0.18680249
Maximum0.20460206
Range0.13832963
Interquartile range (IQR)0.049281556

Descriptive statistics

Standard deviation0.036260348
Coefficient of variation (CV)0.30173196
Kurtosis-0.073138003
Mean0.12017404
Median Absolute Deviation (MAD)0.025047962
Skewness0.58682347
Sum3.244699
Variance0.0013148129
MonotonicityNot monotonic
2023-04-16T14:09:28.271063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.08798686063 1
 
3.7%
0.14108303 1
 
3.7%
0.1270908845 1
 
3.7%
0.2046020638 1
 
3.7%
0.1125031961 1
 
3.7%
0.1263236114 1
 
3.7%
0.1508295626 1
 
3.7%
0.1187957085 1
 
3.7%
0.1411827981 1
 
3.7%
0.07212786828 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.06627243067 1
3.7%
0.07095638428 1
3.7%
0.07212786828 1
3.7%
0.07652597992 1
3.7%
0.08798686063 1
3.7%
0.09124372212 1
3.7%
0.09374774645 1
3.7%
0.09542860289 1
3.7%
0.09637675497 1
3.7%
0.09792360933 1
3.7%
ValueCountFrequency (%)
0.2046020638 1
3.7%
0.1943356199 1
3.7%
0.1692251904 1
3.7%
0.1581093451 1
3.7%
0.1508295626 1
3.7%
0.1479114898 1
3.7%
0.1465566624 1
3.7%
0.1411827981 1
3.7%
0.14108303 1
3.7%
0.1270908845 1
3.7%
Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27285786
Minimum0.15175491
Maximum0.40920413
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:09:28.357008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.15175491
5-th percentile0.15450831
Q10.22048313
median0.27295131
Q30.3134924
95-th percentile0.38810694
Maximum0.40920413
Range0.25744922
Interquartile range (IQR)0.093009268

Descriptive statistics

Standard deviation0.06945117
Coefficient of variation (CV)0.25453241
Kurtosis-0.40089442
Mean0.27285786
Median Absolute Deviation (MAD)0.04938239
Skewness0.045964699
Sum7.3671623
Variance0.0048234651
MonotonicityNot monotonic
2023-04-16T14:09:28.441048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.3050211168 1
 
3.7%
0.2490725097 1
 
3.7%
0.3477466598 1
 
3.7%
0.4092041276 1
 
3.7%
0.2423932498 1
 
3.7%
0.277911945 1
 
3.7%
0.1919648978 1
 
3.7%
0.2821398077 1
 
3.7%
0.2128946956 1
 
3.7%
0.2769710142 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.1517549093 1
3.7%
0.1526909535 1
3.7%
0.1587488241 1
3.7%
0.1919648978 1
3.7%
0.2128946956 1
3.7%
0.217509516 1
3.7%
0.2189090049 1
3.7%
0.222057253 1
3.7%
0.2423932498 1
3.7%
0.2490725097 1
3.7%
ValueCountFrequency (%)
0.4092041276 1
3.7%
0.3952733627 1
3.7%
0.3713853032 1
3.7%
0.3556044904 1
3.7%
0.3477466598 1
3.7%
0.322333696 1
3.7%
0.3168418354 1
3.7%
0.3101429594 1
3.7%
0.3050211168 1
3.7%
0.2926647982 1
3.7%

Outras malformações congênitasprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13884748
Minimum0.054789107
Maximum0.20530267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:09:28.517774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.054789107
5-th percentile0.073401383
Q10.11566708
median0.1307863
Q30.17165448
95-th percentile0.20236072
Maximum0.20530267
Range0.15051357
Interquartile range (IQR)0.055987405

Descriptive statistics

Standard deviation0.041827681
Coefficient of variation (CV)0.30124912
Kurtosis-0.58402626
Mean0.13884748
Median Absolute Deviation (MAD)0.020671091
Skewness0.083243563
Sum3.748882
Variance0.0017495549
MonotonicityNot monotonic
2023-04-16T14:09:28.707779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.2053026748 1
 
3.7%
0.1898524724 1
 
3.7%
0.1985901978 1
 
3.7%
0.1986715692 1
 
3.7%
0.1227307594 1
 
3.7%
0.1307820918 1
 
3.7%
0.1988207871 1
 
3.7%
0.1484946356 1
 
3.7%
0.121013827 1
 
3.7%
0.1101152122 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.05478910685 1
3.7%
0.06761523989 1
3.7%
0.08690238398 1
3.7%
0.09176182708 1
3.7%
0.1099733179 1
3.7%
0.1101152122 1
3.7%
0.1128167936 1
3.7%
0.1185173608 1
3.7%
0.121013827 1
3.7%
0.1220482266 1
3.7%
ValueCountFrequency (%)
0.2053026748 1
3.7%
0.2038778397 1
3.7%
0.1988207871 1
3.7%
0.1986715692 1
3.7%
0.1985901978 1
3.7%
0.1898524724 1
3.7%
0.1822658999 1
3.7%
0.1610430635 1
3.7%
0.1484946356 1
3.7%
0.143382438 1
3.7%

Anomalias cromossômicas não classificadas em outra parteprop
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.049081329
Minimum0.011889664
Maximum0.11731581
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-04-16T14:09:28.788338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.011889664
5-th percentile0.019444435
Q10.026431188
median0.045071032
Q30.070876733
95-th percentile0.088549208
Maximum0.11731581
Range0.10542615
Interquartile range (IQR)0.044445545

Descriptive statistics

Standard deviation0.026784054
Coefficient of variation (CV)0.54570759
Kurtosis-0.11836308
Mean0.049081329
Median Absolute Deviation (MAD)0.021280929
Skewness0.71428931
Sum1.3251959
Variance0.00071738554
MonotonicityNot monotonic
2023-04-16T14:09:28.886544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.1173158142 1
 
3.7%
0.04876944246 1
 
3.7%
0.08073606661 1
 
3.7%
0.03854821492 1
 
3.7%
0.07261569931 1
 
3.7%
0.07728032696 1
 
3.7%
0.02742355684 1
 
3.7%
0.03712365891 1
 
3.7%
0.06947090066 1
 
3.7%
0.03317881941 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.01188966392 1
3.7%
0.01886182367 1
3.7%
0.0208038612 1
3.7%
0.02131349953 1
3.7%
0.02204985862 1
3.7%
0.02379010332 1
3.7%
0.02543881964 1
3.7%
0.02742355684 1
3.7%
0.02973596514 1
3.7%
0.03317881941 1
3.7%
ValueCountFrequency (%)
0.1173158142 1
3.7%
0.08874689386 1
3.7%
0.08808794113 1
3.7%
0.08073606661 1
3.7%
0.07728032696 1
3.7%
0.07261569931 1
3.7%
0.07228256623 1
3.7%
0.06947090066 1
3.7%
0.06121907852 1
3.7%
0.05729416683 1
3.7%

Interactions

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2023-04-16T14:09:19.266998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:09:20.518137image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:09:21.887572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:09:23.116340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-16T14:09:24.380637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-16T14:09:28.973736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Casos_entre_nascidosEspinha bífidapropOutras malformações congênitas do sistema nervosopropMalformações congênitas do aparelho circulatóriopropFenda labial e fenda palatinapropAusência atresia e estenose do intestino delgadopropOutras malformações congênitas do aparelho digestivopropTesticulo não-descidopropOutras malformações do aparelho geniturináriopropDeformidades congênitas do quadrilpropDeformidades congênitas dos péspropOutras malformações e deformidades congênitas do aparelho osteomuscularpropOutras malformações congênitaspropAnomalias cromossômicas não classificadas em outra partepropUFEstado
Casos_entre_nascidos1.0000.6420.5770.4840.5580.1650.3290.6530.7010.2060.6360.6760.6360.6371.0001.000
Espinha bífidaprop0.6421.0000.5170.4440.4980.037-0.0890.6620.6980.2790.5190.5050.2760.3581.0001.000
Outras malformações congênitas do sistema nervosoprop0.5770.5171.0000.0480.380-0.1480.2010.3870.3140.2390.6450.3420.6250.2901.0001.000
Malformações congênitas do aparelho circulatórioprop0.4840.4440.0481.0000.2370.5940.1120.4850.6710.3240.0050.5830.2680.4631.0001.000
Fenda labial e fenda palatinaprop0.5580.4980.3800.2371.000-0.0910.2520.3450.4470.2520.5980.2910.2570.3051.0001.000
Ausência atresia e estenose do intestino delgadoprop0.1650.037-0.1480.594-0.0911.0000.1520.1820.1890.014-0.1930.1380.1200.4931.0001.000
Outras malformações congênitas do aparelho digestivoprop0.329-0.0890.2010.1120.2520.1521.0000.0670.1830.3350.120-0.0930.0820.4241.0001.000
Testiculo não-descidoprop0.6530.6620.3870.4850.3450.1820.0671.0000.7300.4360.4380.5970.4210.2551.0001.000
Outras malformações do aparelho geniturinárioprop0.7010.6980.3140.6710.4470.1890.1830.7301.0000.4890.4990.7000.4160.3931.0001.000
Deformidades congênitas do quadrilprop0.2060.2790.2390.3240.2520.0140.3350.4360.4891.0000.3210.2080.195-0.0581.0001.000
Deformidades congênitas dos pésprop0.6360.5190.6450.0050.598-0.1930.1200.4380.4990.3211.0000.3460.5920.1531.0001.000
Outras malformações e deformidades congênitas do aparelho osteomuscularprop0.6760.5050.3420.5830.2910.138-0.0930.5970.7000.2080.3461.0000.5130.2641.0001.000
Outras malformações congênitasprop0.6360.2760.6250.2680.2570.1200.0820.4210.4160.1950.5920.5131.0000.2801.0001.000
Anomalias cromossômicas não classificadas em outra parteprop0.6370.3580.2900.4630.3050.4930.4240.2550.393-0.0580.1530.2640.2801.0001.0001.000
UF1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Estado1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-04-16T14:09:25.892271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-16T14:09:26.129614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

UFEstadoCasos_entre_nascidosEspinha bífidapropOutras malformações congênitas do sistema nervosopropMalformações congênitas do aparelho circulatóriopropFenda labial e fenda palatinapropAusência atresia e estenose do intestino delgadopropOutras malformações congênitas do aparelho digestivopropTesticulo não-descidopropOutras malformações do aparelho geniturináriopropDeformidades congênitas do quadrilpropDeformidades congênitas dos péspropOutras malformações e deformidades congênitas do aparelho osteomuscularpropOutras malformações congênitaspropAnomalias cromossômicas não classificadas em outra parteprop
0ACAcre0.0091510.0117320.0762550.0586580.0527920.0175970.0527920.0058660.0527920.0000000.0879870.3050210.2053030.117316
1ALAlagoas0.0087630.0282040.0825980.0342480.0785690.0000000.0342480.0161170.0745400.0020150.1692250.3223340.1128170.052379
2AMAmazonas0.0050970.0194560.0583670.0376140.0596640.0000000.0492880.0168620.0337230.0090790.0765260.1517550.0869020.022050
3APAmapá0.0220090.0236660.0946630.0118330.0887470.0000001.4909480.0177490.0414150.0000000.1479110.2189090.1360790.088747
4BABahia0.0073370.0107660.0661370.0492180.0405020.0010250.0328120.0102540.0563960.0015380.0979240.3168420.1327860.029736
5CECeará0.0104060.0348410.0944580.0735530.0642620.0030970.0472290.0379380.1331700.0077420.1943360.2926650.1610430.057294
6DFDistrito Federal0.0088700.0234370.0504800.0612970.0630990.0018030.0540850.0180280.1045650.0108170.0937480.3713850.1099730.045071
7ESEspírito Santo0.0088820.0330330.1101100.0917580.0495490.0000000.0458790.0201870.1046040.0055050.0954290.3101430.1431430.088088
8GOGoiás0.0074720.0215180.0549920.0466230.0645550.0000000.0490140.0107590.0812920.0011950.1159610.2725680.1267200.034669
9MAMaranhão0.0043110.0251490.0556870.0161670.0404180.0000000.0269450.0053890.0359270.0008980.0709560.1526910.0547890.018862
UFEstadoCasos_entre_nascidosEspinha bífidapropOutras malformações congênitas do sistema nervosopropMalformações congênitas do aparelho circulatóriopropFenda labial e fenda palatinapropAusência atresia e estenose do intestino delgadopropOutras malformações congênitas do aparelho digestivopropTesticulo não-descidopropOutras malformações do aparelho geniturináriopropDeformidades congênitas do quadrilpropDeformidades congênitas dos péspropOutras malformações e deformidades congênitas do aparelho osteomuscularpropOutras malformações congênitaspropAnomalias cromossômicas não classificadas em outra parteprop
17PRParaná0.0072610.0227920.0683750.0950740.0859580.0058610.0455840.0065120.0586070.0052100.0963770.2220570.1185170.072283
18RJRio de Janeiro0.0067610.0206770.0586640.0533750.0413530.0028850.0346210.0197150.0726090.0024040.0721280.2769710.1101150.033179
19RNRio Grande do Norte0.0076420.0268920.0829170.0403380.0694710.0000000.0605070.0089640.0851580.0044820.1411830.2128950.1210140.069471
20RORondônia0.0075730.0296990.0816720.0408360.0593980.0037120.0222740.0222740.0445480.0000000.1187960.2821400.1484950.037124
21RRRoraima0.0071990.0137120.0754150.0068560.0548470.0000000.0342790.0000000.0342790.0068560.1508300.1919650.1988210.027424
22RSRio Grande do Sul0.0092590.0260080.0549880.1159200.0780230.0044580.0430990.0170910.1136910.0029720.1263240.2779120.1307820.077280
23SCSanta Catarina0.0083350.0286370.0726160.0930710.0756840.0092050.0593200.0194320.0961390.0040910.1125030.2423930.1227310.072616
24SESergipe0.0110900.0355830.1215750.0474440.0859920.0000000.0415130.0296520.1215750.0088960.2046020.4092040.1986720.038548
25SPSão Paulo0.0126360.0319870.0945910.2718000.0696180.0088950.0974990.0336970.1240120.0283940.1270910.3477470.1985900.080736
26TOTocantins0.0103600.0332860.1081800.1165020.0915370.0000000.0291250.0332860.1081800.0083220.1581090.3952730.2038780.020804